Article Per Year (5 Year)
p-Index From 2020 - 2025
14.451
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Teknosains: Media Informasi Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer SIGMA: Jurnal Pendidikan Matematika AlphaMath: Journal of Mathematics Education JOIV : International Journal on Informatics Visualization AL ISHLAH Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPM (Jurnal Pemberdayaan Masyarakat) Faktor Exacta Jurnal Penjaminan Mutu JMM (Jurnal Masyarakat Mandiri) JTAM (Jurnal Teori dan Aplikasi Matematika) CARADDE: Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURNAL MathEdu (Mathematic Education Journal) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) GERVASI: Jurnal Pengabdian kepada Masyarakat TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Sistem informasi dan informatika (SIMIKA) Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Unri Conference Series: Community Engagement Jurnal Dedikasi Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Online Learning in Educational Research Seminar Nasional Pengabdian Kepada Masyarakat Catimore: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Edutic : Pendidikan dan Informatika Internet of Things and Artificial Intelligence Journal Jurnal Penjaminan Mutu Indonesian Journal of Fundamental Sciences IPTEK: Jurnal Hasil Pengabdian kepada Masyarakat Teknovokasi : Jurnal Pengabdian Masyarakat Vokatek : Jurnal Pengabdian Masyarakat Information Technology Education Journal Pengabdian Jurnal Abdimas Journal of Embedded Systems, Security and Intelligent Systems Ininnawa: Jurnal Pengabdian Masyarakat Jurnal Sipakatau: Inovasi Pengabdian Masyarakat Jurnal Ilmu Pengetahuan dan Teknologi Bagi Masyarakat Jurnal MediaTIK Media Elektrik Malaqbiq : Jurnal Pengabdian kepada Masyarakat. Sasambo: Jurnal Abdimas (Journal of Community Service) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Andi Baso Kaswar Andi Baso Kaswar
Universitas Negeri Makassar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 87 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 87 Documents
Search

 5   6   7   8   9 
Enhancing Computational Thinking Skills through Digital Literacy and Blended Learning: The Mediating Role of Learning Motivation Nirmala, Putri; Suhardi, Iwan; Kaswar, Andi Baso; Surianto, Dewi Fatmarani; B, Muhammad Fajar; Soeharto, Soeharto; Lavicza, Zsolt
Online Learning In Educational Research (OLER) Vol 5, No 1 (2025): Online Learning in Educational Research
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/oler.v5i1.504

Abstract

In the digital era, computational thinking becomes an essential skill to overcome technological challenges in 21st centuryeducation. This study investigates the impact of digital literacy and blended learning on computational thinking skills, focusing on the mediating role of learning motivation. A total of 413 university students from blended learning environments participated, using a structured questionnaire with validated scales for digital literacy, computational thinking, and learning motivation. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test direct and mediation relationships. The results showed that digital literacy and blended learning significantly influenced computational thinking, with learning motivation acting as a mediator that strengthened this relationship. Digital literacy showed a greater influence than blended learning. These findings highlight the importance of integrating digital literacy and motivational strategies into blended learning to optimize the development of computational thinking skills, as well as providing insights for learning design that is relevant to the needs of the 21st century.
EKSPLORASI HUBUNGAN ANTARA LITERASI MATEMATIKA DAN KEMAMPUAN PROBLEM SOLVING PADA SISWA DI ERA DIGITAL Nurjannah, Nurjannah; Kaswar, Andi Baso
SIGMA: JURNAL PENDIDIKAN MATEMATIKA Vol. 17 No. 1: Juni 2025
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/sigma.v17i1.17060

Abstract

Di era digital, literasi matematika dan kemampuan pemecahan masalah menjadi keterampilan penting bagi siswa untuk beradaptasi dalam masyarakat berbasis teknologi. Literasi matematika melibatkan pemahaman konsep dan penerapannya dalam situasi nyata, termasuk pemecahan masalah. Penelitian ini menggunakan metode kuantitatif dengan pendekatan korelasional untuk mengeksplorasi hubungan antara literasi matematika dan kemampuan pemecahan masalah pada siswa di era digital. Sampel terdiri dari 35 siswa kelas XI di SMA Negeri 5 Sinjai yang dipilih dengan teknik cluster sampling, dengan pengumpulan data melalui tes literasi matematika yang diadaptasi dari PISA dan tes pemecahan masalah yang dirancang khusus untuk konteks digital. Uji normalitas dan linearitas memastikan data memenuhi syarat untuk dilanjutkan ke analisis korelasi. Hasil analisis menunjukkan adanya korelasi yang sangat tinggi antara literasi matematika dan kemampuan pemecahan masalah dengan koefisien korelasi Pearson sebesar 0,997 yang mengindikasikan bahwa hampir seluruh variansi kemampuan pemecahan masalah dapat dijelaskan oleh literasi matematika. Temuan ini menegaskan bahwa literasi matematika tidak hanya penting untuk prestasi akademik, tetapi juga mendukung keterampilan kognitif tingkat tinggi yang diperlukan dalam konteks digital. Implikasi dari penelitian ini adalah bahwa pendidikan literasi matematika harus diperkuat dalam kurikulum sekolah dengan pendekatan berbasis proyek dan aplikasi digital, guna mempersiapkan siswa menghadapi tantangan kompleks di masa depan yang semakin berbasis teknologi.
Identifikasi Kualitas Fisik Shuttlecocks Menggunakan Teknologi Pengolahan Citra Digital dengan Jaringan Syaraf Tiruan Farid, Muhammad Miftah; Sam, Muh Hadal Ali; Kaswar, Andi Baso; Andayani, Dyah Darma
TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol Vol 11, No 2 (2025): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v11n2.167-180

Abstract

Shuttlecock merupakan bola yang dipakai dalam permainan bulutangkis, terbuat dari bulu angsa dan bulu ayam berwarna putih. Bola ini memiliki panjang sekitar 64-66 mm, diameter 25 mm, dan berat berkisar antara 4,74 hingga 5,67 gram. Sebelum digunakan pada pertandingan, shuttlecock dipilih berdasarkan kualitas pada bulu dan kepala shuttlecock. Namun, proses pemilihan tersebut masih dilakukan secara manual oleh penyelenggara pertandingan bulutangkis. Jumlah shuttlecock yang banyak memerlukan tenaga kerja yang besar, sehingga seringkali muncul kesalahan manusia akibat kelelahan dan tekanan waktu yang tinggi. Untuk itu, pemanfaatan teknologi menggunakan citra digital dirasa sangat perlu digunakan untuk mengidentifikasi kualitas fisik pada shuttlecock. Oleh karena itu, dalam penelitian ini diusulkan sistem identifikasi kualitas fisik pada shuttlecock menggunakan teknologi pengolahan citra digital dengan metode jaringan syaraf tiruan. Penelitian ini melalui beberapa tahap diantaranya tahap akuisisi citra, preprocessing, segmentasi, morfologi, ekstraksi fitur serta klasifikasi. Penelitian ini juga, mencoba beberapa skenario pelatihan dan pengujian untuk menemukan kombinasi fitur terbaik. Kombinasi warna RGB (channel blue), tekstur (fitur energy), dan bentuk (fitur area dan perimeter) memberikan hasil optimal dalam klasifikasi citra shuttlecock. Hasil penelitian menunjukkan bahwa dengan melatih sistem menggunakan 140 citra latih, diperoleh akurasi tertinggi sebesar 100% dengan waktu komputasi 0,136 detik per citra. Selanjutnya, hasil pengujian pada 60 citra uji mencapai tingkat akurasi sebesar 100% dengan waktu komputasi 0,123 detik per citra. Hasil tersebut menunjukkan bahwa metode yang diusulkan dapat mengidentifikasi kualitas shuttlecock dengan akurat dan waktu komputasi yang cepat. Shuttlecock is a ball used in badminton made of goose feathers and white chicken feathers, has a length of 64-66 mm and has a diameter of 25 mm with a weight of 4,74 – 5,67 grams. Before being used in a match, the shuttlecock is selected based on the quality of the feathers and shuttlecock head. However, the selection process is still done manually by the badminton match organizer. The large number of shuttlecocks requires a large amount of labor, so it is not uncommon for human error to occur due to fatigue and high time pressure. For this reason, the utilization of technology using digital images is deemed very necessary to be used to identify the physical quality of the shuttlecock. Therefore, this research aims to develop a physical quality identification system on shuttlecocks using digital image processing technology with artificial neural network method. This research goes through several stages including image acquisition, preprocessing, segmentation, morphology, feature extraction and classification. This research also tries several training and testing scenarios to find the best combination of features. The combination of RGB color (channel blue), texture (energy feature), and shape (area and perimeter features) provides optimal results in shuttlecock image classification. The results showed that by training the system using 140 training images, the highest accuracy of 100% was obtained with a time of 100%.
Deteksi Tingkat Kematangan Buah Mangga Berdasarkan Fitur Warna Menggunakan Pengolahan Citra Digital Aksa, Muhammad; Ranggareksa, Andi; Aras, Muh Riski Farukhi; Kaswar, Andi Baso; Andayani, Dyah Darma; Intam, Reski Nurul Jariah S
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.10578

Abstract

The classification of mango Golek ripeness is crucial for ensuring product quality and its economic value, especially in industrial applications. Manual and subjective ripeness determination often leads to inconsistency, resulting in decreased harvest quality and market value. This study aims to classify the ripeness of Golek mangoes into three categories: unripe, semi-ripe, and ripe, using digital image processing based on HSV and LAB color features combined with the K-Nearest Neighbor (KNN) algorithm. The dataset consists of 300 images, split into 80% training data and 20% testing data. The proposed method includes image acquisition, preprocessing, segmentation, morphological operations, feature extraction, and classification. The results show that the combination of HSV and LAB color features is effective in distinguishing ripeness levels, with an accuracy of 81.67% on the testing data and an average precision, recall, and F1-Score of 82%. Consistent color patterns in the unripe and semi-ripe categories enhance accuracy, while fluctuations in color intensity in the ripe category pose challenges. This approach shows potential for implementation in automatic sorting systems in industry.
Klasifikasi Tingkat Kualitas Terung dengan Algoritma Backpropagation Berbasis Fitur Warna dan Tekstur R, Muh Raflyawan; Arifky, Reza; Tenriajeng, Andi Afrah; Kaswar, Andi Baso; Andayani, Dyah Darma; Azis, Putri Alysia
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.10655

Abstract

Manual quality assessment of eggplant is often inconsistent, takes a long time, and is prone to errors due to worker fatigue. This research aims to develop an automated system based on digital image processing to assess eggplant quality efficiently and accurately. The stages begin with image capture using a mobile phone device designed to ensure stable lighting and uniform background. The acquired image is then processed through segmentation using the Otsu thresholding method as well as morphological operations to separate the main object from the background. Color and texture features are extracted through Gray-Level Co-occurrence Matrix (GLCM) analysis and RGB, HSV, and LAB color spaces. Training data amounting to 90% of the total dataset was used to train an artificial neural network-based classification model with a backpropagation algorithm, while the remaining 10% was used for testing. Experimental results showed that the combination of LAB, RGB, HSV, and texture features gave the best results, with a testing accuracy of 86%, recall of 85%, and precision of 92%. This model is very effective in detecting poor quality eggplants with 100% accuracy. This system can support the application of technology in the horticultural sector.
KLASIFIKASI BUAH KELAPA BERDASARKAN WARNA KULIT UNTUK MENGIDENTIFIKASI KETEBALAN DAGING PADA BERBAGAI TINGKAT KEMATANGAN MENGGUNAKAN JARINGAN SARAF TIRUAN (JST) Ahmad Khan, Sardar Faroq; Dina Salam, Fitria Nur; Aulia, Magfirah; Kaswar, Andi Baso; Jariah S.Intam, Rezki Nurul; Wahid, Abdul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 4: Agustus 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.124

Abstract

Kelapa (Cocos nucifera L.) adalah bagian dari suku aren-arenan atau Arecaceae dari marga cocos. Kelapa adalah tanaman yang sering ditemui dan kaya akan manfaat bagi umat manusia, mulai dari daun, batang pohon dan buah kelapanya. Pedagang tradisional dapat menggunakan suara yang dihasilkan dari ketukan tangan untuk mengetahui tingkat kematangan buah kelapa. Namun, dengan cara manual ini ada kemungkinan kesalahan dalam proses pengklasifikasianya. Maka dari itu, pada penelitian ini diusulkan judul Klasifikasi Buah Kelapa Berdasarkan Ketebalan Dagingnya Pada Berbagai Tingkat Kematangan Menggunakan Jaringan Saraf Tiruan (JST). Metode penelitian untuk pengklasifikasian terdiri atas 7 tahap yaitu tahap akuisisi citra, preprocessing, segmentasi, operasi morfologi, ekstraksi fitur, klasifikasi, dan evaluasi. Harapan dari metode yang digunakan untuk memberikan solusi khusunya kepada para petani dan pedagang dalam mengklasifikasi atau menyortir buah kelapa untuk mengetahui kualitas dagingnya dengan bantuan teknologi pengolahan citra digital. Dengan menggunakan 300 dataset citra yang dibagi menjadi 240 citra latih dan 60 citra uji, yang menghasilkan tingkat akurasi 97,91% pada citra latih dan 96,66% pada citra uji. Dengan waktu komputasi 0,31 detik per citra pada citra latih dan 0,21 detik per citra pada citra uji. Sehingga hasil dari pembahasan pada penelitian ini, pengklasifikasian buah kelapa menggunakan metode Jaringan Saraf Tiruan (JST) dengan memanfaatkan fitur warna dapat berjalan dan menghasilkan hasil yang dapat digolongkan baik.Abstract Coconut (Cocos nucifera L.) is part of the Arecaceae tribe of the cocos genus. Coconut is a plant that is often encountered and is rich in benefits for mankind, starting from the leaves, tree trunk and coconut fruit. Traditional traders can use the sound produced by hand tapping to determine the ripeness of the coconut fruit. However, with this manual method there is a possibility of error in the classification process. Therefore, this research proposes the title Classification of Coconut Fruit Based on the Thickness of the Flesh at Various Levels of Maturity Using Artificial Neural Networks (JST). The research method for classification consists of 7 stages, namely image acquisition, preprocessing, segmentation, morphological operations, feature extraction, classification, and evaluation. The hope of the method used to provide solutions especially to farmers and traders in classifying or sorting coconut fruit to determine the quality of the meat with the help of digital image processing technology. By using 300 image datasets divided into 240 training images and 60 test images, which resulted in an accuracy rate of 97.91% on the training image and 96.66% on the test image. With a computation time of 0.31 seconds per image on the training image and 0.21 seconds per image on the test image. So that the results of the discussion in this study, the classification of coconut fruit using the Artificial Neural Network (JST) method by utilizing color features can run and produce results that can be classified as good.
Hyperellipsoid Cluster Merging using Hierarchical Analysis of Hyperellipsoid Cluster for Image Segmentation Kaswar, Andi Baso; Nurjannah, Nurjannah; Djawad, Yasser Abd
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2815

Abstract

Segmentation is one of the critical stages in digital image processing and computer vision. However, conventional clustering-based segmentation methods, such as K-means and Fuzzy C-means (FCM), are still unable to accurately segment images whose pixels form hyperellipsoid clusters in the feature space. In addition, previous clustering methods based on Mahalanobis distance measurement require a long computational time and still have the potential to fall into local optima. Therefore, in this paper, we propose a new method for segmenting images whose pixels form hyperellipsoid clusters in the feature space, utilizing hyperellipsoid clusters merging through hierarchical analysis of hyperellipsoid clusters. The proposed method comprises eight main steps: histogram extraction, peak and valley identification, elimination of low peaks and valleys, peak combination for centroid initialization, initialization of cluster pixel members, elimination of ineffective clusters, hyperellipsoid cluster merging, and finalization of cluster members. This paper presents a novel approach to segmenting color images by employing an initial centroid discovery process and cluster analysis that considers cluster covariance for cluster merging. Based on the tests conducted using various image characteristics, the proposed method can provide 97.42% accuracy, 98.02% precision, 97.15% recall, 2.58 misclassification error, 97.54 F1-score, 95.29% intersection over union, 97.52% dice coefficient, and 15.37 seconds of computation time. The test results are superior to those of conventional methods, such as K-means and FCM. Based on these results, it can be concluded that the proposed method can effectively segment images with high accuracy. The proposed method can serve as an alternative approach to image segmentation.
Co-Authors A. Farha Adella A. Muhammad Idkhan A. Mutahharah A. Mutahharah Mutahharah A.Farha Adella Abd. Rahman Patta Abdul Wahid Adiba, Fhatiah Afdhaliyah, Mukhlishah Afyan, Nurbaitul Aglaia, Alifya Nuraisyar Agung, Andi Sadri Agus Zainal Arifin Agus Zainal Arifin Agustinus Suria Darme Ahmad Adzan Lain Ahmad Fudhail  Majid Ahmad Khan, Sardar Faroq Ahmad Mustofa Hadi Ahmad Mustofa Hadi Ainun Zahra Adistia Akbar, Trisakti Aksa, Muhammad Al Imran Alfian Firlansyah Ananta Dwi Prayoga Alwy Andi Akram Nur Risal Andi Alamsyah Rivai Andi Nurul Izzah Andi Rosman N Anggy Heriyanti Anggy Heriyanti Anggy Heriyanti Anny Yuniarti Aqsha, Ismail Aras, Muh Riski Farukhi Arifky, Reza Arinanda Alviansyah Arliandy Arliandy Arsyad, Meisaraswaty Arya Yudhi Wijaya Arya Yudhi Wijaya Aryadi Nurfalaq Ashadi, Ninik Rahayu Astuti, Ninik Aswar, Aswar Atthariq, Muhammad Aulia, Magfirah Awalia, Nur Ayu Futri Azis, Putri Alysia Azis, Salsabila Bantun, Suharsono Bugdady, Andi Jaedil Bukhari Naufal Nur A.G Chairati, Chairati Cyahrani Wulan Purnama Cyahrani Wulan Purnama Rasyid Darma Andayani, Dyah Darme, Agustinus Suria Della Fadhilatunisa Dewi Fatmarani Surianto Dhanendra, Fadhil Dina Salam, Fitria Nur Dini, Juliano Nufiansyach Dirawan, Gufran Darma Edy, Marwan Ramdhany Elva Amalia Elva Amalia Eman Wahyudi Kasim Eriyani, Nindy Sri Fachriansyah, Zaky Farid, Muhammad Miftah Farros Taufiqurrahman Fathahillah Fathahillah Fazli Arif Fhatiah Adiba Fhatiah Adiba Fhatiah Adiba Hafidz Muhtar Hartanto Tantriawan Herman Hermansyah Hermansyah Ibnu Fikrie Syahputra Idkhan, A. Muhammad Ihlasul Amal Ikra Ain Fahwa Ikra Ain Fahwa Ilham, Muh Indri Pratiwi Ramadhani Intam, Reski Nurul Jariah S Irwansyah Suwahyu Ishak Israwati Hamsar Iwan Suhardi Jamaluddin, Bunga Mawar Jamila Jamila Jamila Jariah S.Intam, Rezki Nurul Jasruddin Daud Malago Jayanti Yusmah Sari Jumadi Mabe Parenreng Jusrawati Jusrawati Jusrawati Kaswar, A Baso Kurnia Prima Putra Labusab Labusab Labusab Labusab, Labusab Lapendy, Jessica Crisfin Lavicza, Zsolt M. Miftach Fakhri Makmur, Haerunnisya Marwan Eka Ramdhany Marwan Ramdhany Edy Maulana Muhammad Mawaddah, Arini Ulfa Muammar Muammar Muh Aldhy Fatahillah Muh Devan Fahresi Muh Omar Hassan ST Muh. Dirgafa Anugra Rais Muh. Dirgafa Anugrah Rais Muh. Fardika Pratama Putra Muh. Fauzan Arifuddin Muh. Rais Muh. Rasul D Muhammad Agung Muhammad Agung Muhammad Akbar Muhammad Akil, Muhammad Muhammad Fajar B Muhammad Fajar B Muhammad Naim Muhammad Nur Yusri Maulidin Yusuf Muhammad Nur Yusri Maulidin Yusuf Muhammad Yahya Muhiddin Palennari Muhira Muhira Muhtar, Hafidz Mukhtar Mukhtar Mulia, Musda Rida Muliaty Yantahin Musdar, Devi Miftahul Jannah Mustari Lamada Mutahharah, A Naim, Muhammad Nasrullah, Asmaul Husnah NFH, Alifya NIRMALA, PUTRI Nirsal Novianti, Andi Fitri Nur Anny S. Taufieq Nur Fadillah Bustamin Nur Inayah Yusuf Nurfalaq, Aryadi Nurhidayat Nurhidayat Nurhikma Nurhikma Nurhikma Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurul Amanda Pratiwi Hasbullah Nurul Isra Humaira B Nurul Istiqamah Qalbi Nurul Izzah Dwi Nurul Izzah Dwi Nurdinah Patongai, Dian Dwi Putri Ulan Sari R, Muh Raflyawan R, Ranir Aftar Ranggareksa, Andi Ranir Atfar R Rapa, Wiwi Resky, Andi Aulia Cahyana Riana T. Mangesa Riana T. Mangesa Ridwan Daud Mahande Ridwansyah Rivai, Andi Tenri Ola Rosidah RR. Ella Evrita Hestiandari Rusli, Risvan Sahribulan Sahribulan Saiful Bahri Musa Sam, Muh Hadal Ali Sanatang Saparuddin Saparuddin Saprina Mamase Sartika Sari Sartika Sari Sasmita Sasmita Sasmita SATRIYAS ILYAS Silvia Andriani Soeharto Soeharto Sri Rahayu St. Fatmah Hiola Suharsono Bantun Suhartono, Suhartono Supria Supria Surianto, Dewi Fatmawati Susiana Sari Syamsuddin Tenriajeng, Andi Afrah Tenriola, Andi Tsabita Syalza Billa Tsabita Syalza Billa Irawan Umar, Nur Fadhilah Wahda Arfiana AR WAHYUDI Wanda Hamidah Wardani, Ayu Tri Wiwi Rapa WULANDARI Yasser Abd Djawad Yusuf, Zulfatni